Benn Emma K T, Goldfeld Keith S
Center for Biostatistics and Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai.
Department of Population Health, New York University School of Medicine.
Health Psychol. 2016 Apr;35(4):403-6. doi: 10.1037/hea0000309.
Moving from a descriptive focus to a comprehensive analysis grounded in causal inference can be particularly daunting for disparities researchers. However, even a simple model supported by the theoretical underpinnings of causality gives researchers a better chance to make correct inferences about possible interventions that can benefit our most vulnerable populations. This commentary provides a brief description of how race/ethnicity and context relate to questions of causality, and uses a hypothetical scenario to explore how different researchers might analyze the data to estimate causal effects of interest. Perhaps although not entirely removed of bias, these causal estimates will move us a step closer to understanding how to intervene. (PsycINFO Database Record
对于差异研究人员而言，从描述性研究转向基于因果推断的全面分析可能格外艰巨。然而，即使是一个有因果关系理论基础支持的简单模型，也能让研究人员更有机会对可能有益于最弱势群体的干预措施做出正确推断。本评论简要描述了种族/民族与背景如何与因果关系问题相关联，并使用一个假设情景来探讨不同的研究人员可能如何分析数据以估计感兴趣的因果效应。或许这些因果估计虽然不能完全消除偏差，但会使我们在理解如何进行干预方面更进了一步。（《心理学文摘数据库记录》 ）
